Overview

Dataset statistics

Number of variables66
Number of observations4562
Missing cells137406
Missing cells (%)45.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory514.0 B

Variable types

Numeric27
DateTime1
Categorical5
Boolean33

Warnings

alt is highly correlated with astHigh correlation
ast is highly correlated with altHigh correlation
bilirubin_direct is highly correlated with bilirubin_totalHigh correlation
bilirubin_total is highly correlated with bilirubin_directHigh correlation
haematocrit_percent is highly correlated with haemoglobinHigh correlation
haemoglobin is highly correlated with haematocrit_percentHigh correlation
pcr_dengue_load is highly correlated with pcr_dengue_reactionHigh correlation
pcr_dengue_reaction is highly correlated with pcr_dengue_loadHigh correlation
bleeding_vaginal is highly correlated with hematuriaHigh correlation
hematuria is highly correlated with bleeding_vaginalHigh correlation
abdominal_distension has 3860 (84.6%) missing values Missing
abdominal_pain has 94 (2.1%) missing values Missing
age has 78 (1.7%) missing values Missing
albumin has 3006 (65.9%) missing values Missing
alt has 2852 (62.5%) missing values Missing
ascites has 91 (2.0%) missing values Missing
ast has 2853 (62.5%) missing values Missing
bilirubin_direct has 4206 (92.2%) missing values Missing
bilirubin_total has 4207 (92.2%) missing values Missing
bleeding_gi has 481 (10.5%) missing values Missing
bleeding_gum has 93 (2.0%) missing values Missing
bleeding_mucosal has 251 (5.5%) missing values Missing
bleeding_nose has 96 (2.1%) missing values Missing
bleeding_skin has 316 (6.9%) missing values Missing
bleeding_vaginal has 2086 (45.7%) missing values Missing
body_temperature has 3892 (85.3%) missing values Missing
bruising has 94 (2.1%) missing values Missing
cough has 3863 (84.7%) missing values Missing
creatinine has 3675 (80.6%) missing values Missing
diarrhoea has 107 (2.3%) missing values Missing
ecchymosis has 3851 (84.4%) missing values Missing
effusion has 3852 (84.4%) missing values Missing
gender has 78 (1.7%) missing values Missing
haematocrit_percent has 551 (12.1%) missing values Missing
haemoglobin has 764 (16.7%) missing values Missing
headache has 3865 (84.7%) missing values Missing
heart_sound has 3855 (84.5%) missing values Missing
height has 575 (12.6%) missing values Missing
hematemesis has 3852 (84.4%) missing values Missing
hematoma has 3851 (84.4%) missing values Missing
hematuria has 3852 (84.4%) missing values Missing
igg has 3110 (68.2%) missing values Missing
igg_interpretation has 3110 (68.2%) missing values Missing
igm has 3110 (68.2%) missing values Missing
igm_interpretation has 3110 (68.2%) missing values Missing
jaundice has 88 (1.9%) missing values Missing
joint_pain has 3878 (85.0%) missing values Missing
liver_palpation_size has 3859 (84.6%) missing values Missing
lymphocytes_percent has 724 (15.9%) missing values Missing
melaena has 3852 (84.4%) missing values Missing
muscle_pain has 3878 (85.0%) missing values Missing
neutrophils_percent has 724 (15.9%) missing values Missing
oedema has 3852 (84.4%) missing values Missing
parental_fluid has 101 (2.2%) missing values Missing
parental_fluid_period has 4460 (97.8%) missing values Missing
parental_fluid_volume has 4460 (97.8%) missing values Missing
pcr_dengue_load has 4355 (95.5%) missing values Missing
pcr_dengue_reaction has 4355 (95.5%) missing values Missing
petechiae has 88 (1.9%) missing values Missing
pleural_effusion has 88 (1.9%) missing values Missing
plt has 556 (12.2%) missing values Missing
pulse has 3884 (85.1%) missing values Missing
restlessness has 3868 (84.8%) missing values Missing
runny_nose has 3870 (84.8%) missing values Missing
serotype has 3909 (85.7%) missing values Missing
skin_rash has 102 (2.2%) missing values Missing
sore_throat has 3873 (84.9%) missing values Missing
vomiting has 102 (2.2%) missing values Missing
wbc has 711 (15.6%) missing values Missing
weight has 182 (4.0%) missing values Missing
liver_palpation_size has 487 (10.7%) zeros Zeros
day_from_enrolment has 740 (16.2%) zeros Zeros
day_from_admission has 740 (16.2%) zeros Zeros

Reproduction

Analysis started2021-02-12 10:54:06.890331
Analysis finished2021-02-12 10:55:28.906686
Duration1 minute and 22.02 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

study_no
Real number (ℝ≥0)

Distinct740
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369.9302937
Minimum1
Maximum740
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:28.982356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1182
median370
Q3559
95-th percentile706
Maximum740
Range739
Interquartile range (IQR)377

Descriptive statistics

Standard deviation216.8060228
Coefficient of variation (CV)0.5860726372
Kurtosis-1.226664614
Mean369.9302937
Median Absolute Deviation (MAD)188
Skewness0.01104943695
Sum1687622
Variance47004.85153
MonotocityIncreasing
2021-02-12T10:55:29.109049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68823
 
0.5%
73519
 
0.4%
19916
 
0.4%
10416
 
0.4%
215
 
0.3%
18214
 
0.3%
7314
 
0.3%
12314
 
0.3%
60213
 
0.3%
9212
 
0.3%
Other values (730)4406
96.6%
ValueCountFrequency (%)
17
0.2%
215
0.3%
38
0.2%
47
0.2%
57
0.2%
ValueCountFrequency (%)
7401
 
< 0.1%
7392
 
< 0.1%
7389
0.2%
7377
0.2%
7362
 
< 0.1%

date
Date

Distinct682
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
Minimum2006-04-22 00:00:00
Maximum2008-11-18 00:00:00
2021-02-12T10:55:29.229844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:55:29.357650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

abdominal_distension
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing3860
Missing (%)84.6%
Memory size35.8 KiB
V_0
696 
V_1
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2106
Distinct characters4
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV_0
2nd rowV_1
3rd rowV_0
4th rowV_0
5th rowV_0
ValueCountFrequency (%)
V_0696
 
15.3%
V_16
 
0.1%
(Missing)3860
84.6%
2021-02-12T10:55:29.565892image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:55:29.621240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
v_0696
99.1%
v_16
 
0.9%

Most occurring characters

ValueCountFrequency (%)
V702
33.3%
_702
33.3%
0696
33.0%
16
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter702
33.3%
Connector Punctuation702
33.3%
Decimal Number702
33.3%

Most frequent character per category

ValueCountFrequency (%)
0696
99.1%
16
 
0.9%
ValueCountFrequency (%)
V702
100.0%
ValueCountFrequency (%)
_702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1404
66.7%
Latin702
33.3%

Most frequent character per script

ValueCountFrequency (%)
_702
50.0%
0696
49.6%
16
 
0.4%
ValueCountFrequency (%)
V702
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2106
100.0%

Most frequent character per block

ValueCountFrequency (%)
V702
33.3%
_702
33.3%
0696
33.0%
16
 
0.3%

abdominal_pain
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing94
Missing (%)2.1%
Memory size35.8 KiB
True
2757 
False
1711 
(Missing)
 
94
ValueCountFrequency (%)
True2757
60.4%
False1711
37.5%
(Missing)94
 
2.1%
2021-02-12T10:55:29.661121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

age
Real number (ℝ≥0)

MISSING

Distinct37
Distinct (%)0.8%
Missing78
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean23.05999108
Minimum15
Maximum57
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:29.744755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q118
median21
Q326
95-th percentile36
Maximum57
Range42
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.911454613
Coefficient of variation (CV)0.2997162743
Kurtosis2.538138479
Mean23.05999108
Median Absolute Deviation (MAD)4
Skewness1.438556641
Sum103401
Variance47.76820486
MonotocityNot monotonic
2021-02-12T10:55:29.846423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
17394
 
8.6%
20358
 
7.8%
18339
 
7.4%
19336
 
7.4%
23314
 
6.9%
15299
 
6.6%
16285
 
6.2%
24280
 
6.1%
21278
 
6.1%
22192
 
4.2%
Other values (27)1409
30.9%
ValueCountFrequency (%)
15299
6.6%
16285
6.2%
17394
8.6%
18339
7.4%
19336
7.4%
ValueCountFrequency (%)
576
 
0.1%
546
 
0.1%
4924
0.5%
488
 
0.2%
478
 
0.2%

albumin
Real number (ℝ≥0)

MISSING

Distinct250
Distinct (%)16.1%
Missing3006
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean36.9361825
Minimum19
Maximum55
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:29.958063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile27
Q133.79999924
median37.09999847
Q340
95-th percentile45.40000153
Maximum55
Range36
Interquartile range (IQR)6.200000763

Descriptive statistics

Standard deviation5.439425737
Coefficient of variation (CV)0.1472655095
Kurtosis0.3491069559
Mean36.9361825
Median Absolute Deviation (MAD)3.099998474
Skewness-0.2319055739
Sum57472.69998
Variance29.58735234
MonotocityNot monotonic
2021-02-12T10:55:30.078940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4095
 
2.1%
3893
 
2.0%
3772
 
1.6%
3668
 
1.5%
3959
 
1.3%
3555
 
1.2%
3450
 
1.1%
3346
 
1.0%
4139
 
0.9%
3237
 
0.8%
Other values (240)942
 
20.6%
(Missing)3006
65.9%
ValueCountFrequency (%)
191
 
< 0.1%
19.399999621
 
< 0.1%
19.899999621
 
< 0.1%
203
0.1%
212
< 0.1%
ValueCountFrequency (%)
551
< 0.1%
542
< 0.1%
53.599998471
< 0.1%
51.900001531
< 0.1%
51.799999241
< 0.1%

alt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct433
Distinct (%)25.3%
Missing2852
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean155.6181871
Minimum3
Maximum9917
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:30.196609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile18
Q138
median78
Q3162
95-th percentile471
Maximum9917
Range9914
Interquartile range (IQR)124

Descriptive statistics

Standard deviation368.8033335
Coefficient of variation (CV)2.369924366
Kurtosis323.9863872
Mean155.6181871
Median Absolute Deviation (MAD)49
Skewness14.80782646
Sum266107.1
Variance136015.8988
MonotocityNot monotonic
2021-02-12T10:55:30.321343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2424
 
0.5%
2224
 
0.5%
4023
 
0.5%
7022
 
0.5%
2821
 
0.5%
8021
 
0.5%
3520
 
0.4%
2019
 
0.4%
1919
 
0.4%
1719
 
0.4%
Other values (423)1498
32.8%
(Missing)2852
62.5%
ValueCountFrequency (%)
31
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
103
0.1%
117
0.2%
ValueCountFrequency (%)
99171
< 0.1%
50481
< 0.1%
42371
< 0.1%
35961
< 0.1%
28811
< 0.1%

ascites
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing91
Missing (%)2.0%
Memory size35.8 KiB
False
4178 
True
 
293
(Missing)
 
91
ValueCountFrequency (%)
False4178
91.6%
True293
 
6.4%
(Missing)91
 
2.0%
2021-02-12T10:55:30.387752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ast
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct433
Distinct (%)25.3%
Missing2853
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean164.0005266
Minimum10
Maximum8680
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:30.467142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile18
Q140
median89
Q3171
95-th percentile481.2
Maximum8680
Range8670
Interquartile range (IQR)131

Descriptive statistics

Standard deviation383.6124299
Coefficient of variation (CV)2.339092671
Kurtosis250.6484605
Mean164.0005266
Median Absolute Deviation (MAD)57
Skewness13.54247353
Sum280276.9
Variance147158.4964
MonotocityNot monotonic
2021-02-12T10:55:30.583824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4031
 
0.7%
2031
 
0.7%
2525
 
0.5%
8025
 
0.5%
2424
 
0.5%
2919
 
0.4%
1618
 
0.4%
2618
 
0.4%
3018
 
0.4%
4717
 
0.4%
Other values (423)1483
32.5%
(Missing)2853
62.5%
ValueCountFrequency (%)
103
0.1%
114
0.1%
127
0.2%
136
0.1%
13.51
 
< 0.1%
ValueCountFrequency (%)
86801
< 0.1%
76911
< 0.1%
49211
< 0.1%
35841
< 0.1%
31971
< 0.1%

bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct50
Distinct (%)14.0%
Missing4206
Missing (%)92.2%
Infinite0
Infinite (%)0.0%
Mean8.23988764
Minimum0.6999999881
Maximum265
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:30.688358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.6999999881
5-th percentile2
Q12
median3
Q35.25
95-th percentile27.5
Maximum265
Range264.3
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation22.6610116
Coefficient of variation (CV)2.750160268
Kurtosis70.20183181
Mean8.23988764
Median Absolute Deviation (MAD)1
Skewness7.734332535
Sum2933.4
Variance513.5214468
MonotocityNot monotonic
2021-02-12T10:55:30.808834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2143
 
3.1%
356
 
1.2%
435
 
0.8%
522
 
0.5%
622
 
0.5%
109
 
0.2%
87
 
0.2%
134
 
0.1%
204
 
0.1%
94
 
0.1%
Other values (40)50
 
1.1%
(Missing)4206
92.2%
ValueCountFrequency (%)
0.69999998811
 
< 0.1%
12
 
< 0.1%
2143
3.1%
2.53
 
0.1%
2.7999999521
 
< 0.1%
ValueCountFrequency (%)
2651
< 0.1%
1901
< 0.1%
1861
< 0.1%
1281
< 0.1%
901
< 0.1%

bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct67
Distinct (%)18.9%
Missing4207
Missing (%)92.2%
Infinite0
Infinite (%)0.0%
Mean20.30169014
Minimum2
Maximum336
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:30.924439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median12
Q318
95-th percentile60
Maximum336
Range334
Interquartile range (IQR)10

Descriptive statistics

Standard deviation31.87283521
Coefficient of variation (CV)1.569959693
Kurtosis43.62692939
Mean20.30169014
Median Absolute Deviation (MAD)4
Skewness5.917337413
Sum7207.099998
Variance1015.877624
MonotocityNot monotonic
2021-02-12T10:55:31.043033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1052
 
1.1%
843
 
0.9%
1222
 
0.5%
1421
 
0.5%
619
 
0.4%
718
 
0.4%
1616
 
0.4%
1114
 
0.3%
1314
 
0.3%
914
 
0.3%
Other values (57)122
 
2.7%
(Missing)4207
92.2%
ValueCountFrequency (%)
21
 
< 0.1%
41
 
< 0.1%
4.6999998091
 
< 0.1%
58
0.2%
619
0.4%
ValueCountFrequency (%)
3361
< 0.1%
2631
< 0.1%
2061
< 0.1%
2051
< 0.1%
1551
< 0.1%

bleeding_gi
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing481
Missing (%)10.5%
Memory size35.8 KiB
False
2966 
True
1115 
(Missing)
481 
ValueCountFrequency (%)
False2966
65.0%
True1115
 
24.4%
(Missing)481
 
10.5%
2021-02-12T10:55:31.119060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_gum
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing93
Missing (%)2.0%
Memory size35.8 KiB
False
3487 
True
982 
(Missing)
 
93
ValueCountFrequency (%)
False3487
76.4%
True982
 
21.5%
(Missing)93
 
2.0%
2021-02-12T10:55:31.161753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_mucosal
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing251
Missing (%)5.5%
Memory size35.8 KiB
True
2602 
False
1709 
(Missing)
 
251
ValueCountFrequency (%)
True2602
57.0%
False1709
37.5%
(Missing)251
 
5.5%
2021-02-12T10:55:31.196950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_nose
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing96
Missing (%)2.1%
Memory size35.8 KiB
False
3969 
True
497 
(Missing)
 
96
ValueCountFrequency (%)
False3969
87.0%
True497
 
10.9%
(Missing)96
 
2.1%
2021-02-12T10:55:31.240532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_skin
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing316
Missing (%)6.9%
Memory size35.8 KiB
True
3724 
False
522 
(Missing)
 
316
ValueCountFrequency (%)
True3724
81.6%
False522
 
11.4%
(Missing)316
 
6.9%
2021-02-12T10:55:31.275365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

bleeding_vaginal
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing2086
Missing (%)45.7%
Memory size35.8 KiB
False
1642 
True
834 
(Missing)
2086 
ValueCountFrequency (%)
False1642
36.0%
True834
 
18.3%
(Missing)2086
45.7%
2021-02-12T10:55:31.309210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

body_temperature
Real number (ℝ≥0)

MISSING

Distinct21
Distinct (%)3.1%
Missing3892
Missing (%)85.3%
Infinite0
Infinite (%)0.0%
Mean37.63179104
Minimum37
Maximum40.5
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:31.374790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile37
Q137
median37
Q338
95-th percentile39.5
Maximum40.5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8652680886
Coefficient of variation (CV)0.02299300843
Kurtosis0.5040228798
Mean37.63179104
Median Absolute Deviation (MAD)0
Skewness1.246964018
Sum25213.3
Variance0.7486888651
MonotocityNot monotonic
2021-02-12T10:55:31.475480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
37361
 
7.9%
3868
 
1.5%
37.565
 
1.4%
3946
 
1.0%
38.538
 
0.8%
39.528
 
0.6%
37.816
 
0.4%
4013
 
0.3%
37.26
 
0.1%
38.74
 
0.1%
Other values (11)25
 
0.5%
(Missing)3892
85.3%
ValueCountFrequency (%)
37361
7.9%
37.26
 
0.1%
37.43
 
0.1%
37.565
 
1.4%
37.71
 
< 0.1%
ValueCountFrequency (%)
40.53
 
0.1%
40.31
 
< 0.1%
4013
0.3%
39.81
 
< 0.1%
39.528
0.6%

bruising
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing94
Missing (%)2.1%
Memory size35.8 KiB
True
2678 
False
1790 
(Missing)
 
94
ValueCountFrequency (%)
True2678
58.7%
False1790
39.2%
(Missing)94
 
2.1%
2021-02-12T10:55:31.549144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

cough
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3863
Missing (%)84.7%
Memory size35.8 KiB
False
499 
True
 
200
(Missing)
3863 
ValueCountFrequency (%)
False499
 
10.9%
True200
 
4.4%
(Missing)3863
84.7%
2021-02-12T10:55:31.585634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

creatinine
Real number (ℝ≥0)

MISSING

Distinct156
Distinct (%)17.6%
Missing3675
Missing (%)80.6%
Infinite0
Infinite (%)0.0%
Mean96.8962796
Minimum8
Maximum986
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:31.667723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile53
Q168
median83
Q399
95-th percentile164.7
Maximum986
Range978
Interquartile range (IQR)31

Descriptive statistics

Standard deviation82.59219787
Coefficient of variation (CV)0.8523773896
Kurtosis61.51677757
Mean96.8962796
Median Absolute Deviation (MAD)15
Skewness7.152659866
Sum85947
Variance6821.471149
MonotocityNot monotonic
2021-02-12T10:55:31.795607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7422
 
0.5%
8221
 
0.5%
7320
 
0.4%
9319
 
0.4%
8318
 
0.4%
7018
 
0.4%
7517
 
0.4%
7117
 
0.4%
6817
 
0.4%
6017
 
0.4%
Other values (146)701
 
15.4%
(Missing)3675
80.6%
ValueCountFrequency (%)
81
< 0.1%
211
< 0.1%
271
< 0.1%
301
< 0.1%
342
< 0.1%
ValueCountFrequency (%)
9861
< 0.1%
9391
< 0.1%
9371
< 0.1%
8681
< 0.1%
7721
< 0.1%

diarrhoea
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing107
Missing (%)2.3%
Memory size35.8 KiB
False
3128 
True
1327 
(Missing)
 
107
ValueCountFrequency (%)
False3128
68.6%
True1327
29.1%
(Missing)107
 
2.3%
2021-02-12T10:55:31.864118image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

ecchymosis
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3851
Missing (%)84.4%
Memory size35.8 KiB
False
680 
True
 
31
(Missing)
3851 
ValueCountFrequency (%)
False680
 
14.9%
True31
 
0.7%
(Missing)3851
84.4%
2021-02-12T10:55:31.902169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

effusion
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3852
Missing (%)84.4%
Memory size35.8 KiB
False
637 
True
 
73
(Missing)
3852 
ValueCountFrequency (%)
False637
 
14.0%
True73
 
1.6%
(Missing)3852
84.4%
2021-02-12T10:55:31.944069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

gender
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing78
Missing (%)1.7%
Memory size35.8 KiB
Female
2272 
Male
2212 

Length

Max length6
Median length6
Mean length5.01338091
Min length4

Characters and Unicode

Total characters22480
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
Female2272
49.8%
Male2212
48.5%
(Missing)78
 
1.7%
2021-02-12T10:55:32.112235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:55:32.177452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
female2272
50.7%
male2212
49.3%

Most occurring characters

ValueCountFrequency (%)
e6756
30.1%
a4484
19.9%
l4484
19.9%
F2272
 
10.1%
m2272
 
10.1%
M2212
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17996
80.1%
Uppercase Letter4484
 
19.9%

Most frequent character per category

ValueCountFrequency (%)
e6756
37.5%
a4484
24.9%
l4484
24.9%
m2272
 
12.6%
ValueCountFrequency (%)
F2272
50.7%
M2212
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin22480
100.0%

Most frequent character per script

ValueCountFrequency (%)
e6756
30.1%
a4484
19.9%
l4484
19.9%
F2272
 
10.1%
m2272
 
10.1%
M2212
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII22480
100.0%

Most frequent character per block

ValueCountFrequency (%)
e6756
30.1%
a4484
19.9%
l4484
19.9%
F2272
 
10.1%
m2272
 
10.1%
M2212
 
9.8%

haematocrit_percent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct459
Distinct (%)11.4%
Missing551
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean41.43557716
Minimum15.1
Maximum67.90000153
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:32.257072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile31.5
Q138
median41.40000153
Q345
95-th percentile51
Maximum67.90000153
Range52.80000153
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.009680421
Coefficient of variation (CV)0.1450367253
Kurtosis1.409417247
Mean41.43557716
Median Absolute Deviation (MAD)3.5
Skewness-0.1119346583
Sum166198.1
Variance36.11625876
MonotocityNot monotonic
2021-02-12T10:55:32.386842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4351
 
1.1%
4248
 
1.1%
40.544
 
1.0%
42.540
 
0.9%
4138
 
0.8%
4538
 
0.8%
4038
 
0.8%
44.0999984734
 
0.7%
39.2000007634
 
0.7%
40.2000007633
 
0.7%
Other values (449)3613
79.2%
(Missing)551
 
12.1%
ValueCountFrequency (%)
15.11
< 0.1%
15.51
< 0.1%
15.899999621
< 0.1%
161
< 0.1%
19.050000191
< 0.1%
ValueCountFrequency (%)
67.900001531
< 0.1%
671
< 0.1%
651
< 0.1%
642
< 0.1%
63.51
< 0.1%

haemoglobin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct179
Distinct (%)4.7%
Missing764
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean14.24827277
Minimum4.199999809
Maximum23
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:32.507254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4.199999809
5-th percentile10.69999981
Q113
median14.19999981
Q315.60000038
95-th percentile17.70000076
Maximum23
Range18.80000019
Interquartile range (IQR)2.600000381

Descriptive statistics

Standard deviation2.149234169
Coefficient of variation (CV)0.1508417338
Kurtosis1.220488943
Mean14.24827277
Median Absolute Deviation (MAD)1.300000191
Skewness-0.07360177553
Sum54114.93998
Variance4.619207513
MonotocityNot monotonic
2021-02-12T10:55:32.640652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.3999996290
 
2.0%
13.587
 
1.9%
1484
 
1.8%
13.8000001982
 
1.8%
14.1000003882
 
1.8%
13.8999996281
 
1.8%
15.1000003879
 
1.7%
13.1999998179
 
1.7%
14.1999998179
 
1.7%
12.8999996276
 
1.7%
Other values (169)2979
65.3%
(Missing)764
 
16.7%
ValueCountFrequency (%)
4.1999998091
< 0.1%
51
< 0.1%
5.0999999051
< 0.1%
5.5999999051
< 0.1%
6.3000001911
< 0.1%
ValueCountFrequency (%)
231
 
< 0.1%
22.399999622
< 0.1%
22.299999241
 
< 0.1%
22.200000761
 
< 0.1%
21.700000763
0.1%

headache
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3865
Missing (%)84.7%
Memory size35.8 KiB
True
623 
False
 
74
(Missing)
3865 
ValueCountFrequency (%)
True623
 
13.7%
False74
 
1.6%
(Missing)3865
84.7%
2021-02-12T10:55:32.717861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

heart_sound
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3855
Missing (%)84.5%
Memory size35.8 KiB
True
680 
False
 
27
(Missing)
3855 
ValueCountFrequency (%)
True680
 
14.9%
False27
 
0.6%
(Missing)3855
84.5%
2021-02-12T10:55:32.755009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

height
Real number (ℝ≥0)

MISSING

Distinct47
Distinct (%)1.2%
Missing575
Missing (%)12.6%
Infinite0
Infinite (%)0.0%
Mean159.708929
Minimum72
Maximum182
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:32.834324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile150
Q1155
median160
Q3165
95-th percentile172
Maximum182
Range110
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.692583616
Coefficient of variation (CV)0.06068905274
Kurtosis15.31480375
Mean159.708929
Median Absolute Deviation (MAD)5
Skewness-2.428477975
Sum636759.5
Variance93.94617715
MonotocityNot monotonic
2021-02-12T10:55:32.960740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
160345
 
7.6%
158322
 
7.1%
165316
 
6.9%
155306
 
6.7%
170262
 
5.7%
150211
 
4.6%
153207
 
4.5%
168175
 
3.8%
152163
 
3.6%
162162
 
3.6%
Other values (37)1518
33.3%
(Missing)575
 
12.6%
ValueCountFrequency (%)
725
 
0.1%
987
0.2%
105.55
 
0.1%
1065
 
0.1%
10813
0.3%
ValueCountFrequency (%)
1825
 
0.1%
1796
 
0.1%
1779
 
0.2%
17610
 
0.2%
175106
2.3%

hematemesis
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3852
Missing (%)84.4%
Memory size35.8 KiB
False
665 
True
 
45
(Missing)
3852 
ValueCountFrequency (%)
False665
 
14.6%
True45
 
1.0%
(Missing)3852
84.4%
2021-02-12T10:55:33.032831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hematoma
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3851
Missing (%)84.4%
Memory size35.8 KiB
False
696 
True
 
15
(Missing)
3851 
ValueCountFrequency (%)
False696
 
15.3%
True15
 
0.3%
(Missing)3851
84.4%
2021-02-12T10:55:33.076154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

hematuria
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing3852
Missing (%)84.4%
Memory size35.8 KiB
False
709 
True
 
1
(Missing)
3852 
ValueCountFrequency (%)
False709
 
15.5%
True1
 
< 0.1%
(Missing)3852
84.4%
2021-02-12T10:55:33.110007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

igg
Real number (ℝ)

MISSING

Distinct1218
Distinct (%)83.9%
Missing3110
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean28.95542011
Minimum-0.02
Maximum77.95
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:33.188021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0.8555
Q17.5425
median35.655
Q343.2375
95-th percentile53.788
Maximum77.95
Range77.97
Interquartile range (IQR)35.695

Descriptive statistics

Standard deviation18.61978689
Coefficient of variation (CV)0.6430501377
Kurtosis-1.252865447
Mean28.95542011
Median Absolute Deviation (MAD)11.985
Skewness-0.2696457005
Sum42043.27
Variance346.6964637
MonotocityNot monotonic
2021-02-12T10:55:33.322065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.545
 
0.1%
2.595
 
0.1%
41.234
 
0.1%
0.964
 
0.1%
1.214
 
0.1%
40.364
 
0.1%
0.044
 
0.1%
0.434
 
0.1%
6.323
 
0.1%
1.33
 
0.1%
Other values (1208)1412
31.0%
(Missing)3110
68.2%
ValueCountFrequency (%)
-0.021
 
< 0.1%
-0.012
< 0.1%
0.011
 
< 0.1%
0.044
0.1%
0.051
 
< 0.1%
ValueCountFrequency (%)
77.951
< 0.1%
71.271
< 0.1%
70.881
< 0.1%
69.941
< 0.1%
69.361
< 0.1%

igg_interpretation
Categorical

MISSING

Distinct3
Distinct (%)0.2%
Missing3110
Missing (%)68.2%
Memory size35.8 KiB
Positive
1033 
Negative
373 
Equivocal
 
46

Length

Max length9
Median length8
Mean length8.031680441
Min length8

Characters and Unicode

Total characters11662
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive
ValueCountFrequency (%)
Positive1033
 
22.6%
Negative373
 
8.2%
Equivocal46
 
1.0%
(Missing)3110
68.2%
2021-02-12T10:55:33.533974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:55:33.595036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive1033
71.1%
negative373
 
25.7%
equivocal46
 
3.2%

Most occurring characters

ValueCountFrequency (%)
i2485
21.3%
e1779
15.3%
v1452
12.5%
t1406
12.1%
o1079
9.3%
P1033
8.9%
s1033
8.9%
a419
 
3.6%
N373
 
3.2%
g373
 
3.2%
Other values (5)230
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10210
87.5%
Uppercase Letter1452
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
i2485
24.3%
e1779
17.4%
v1452
14.2%
t1406
13.8%
o1079
10.6%
s1033
10.1%
a419
 
4.1%
g373
 
3.7%
q46
 
0.5%
u46
 
0.5%
Other values (2)92
 
0.9%
ValueCountFrequency (%)
P1033
71.1%
N373
 
25.7%
E46
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin11662
100.0%

Most frequent character per script

ValueCountFrequency (%)
i2485
21.3%
e1779
15.3%
v1452
12.5%
t1406
12.1%
o1079
9.3%
P1033
8.9%
s1033
8.9%
a419
 
3.6%
N373
 
3.2%
g373
 
3.2%
Other values (5)230
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11662
100.0%

Most frequent character per block

ValueCountFrequency (%)
i2485
21.3%
e1779
15.3%
v1452
12.5%
t1406
12.1%
o1079
9.3%
P1033
8.9%
s1033
8.9%
a419
 
3.6%
N373
 
3.2%
g373
 
3.2%
Other values (5)230
 
2.0%

igm
Real number (ℝ≥0)

MISSING

Distinct1275
Distinct (%)87.8%
Missing3110
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean26.24436295
Minimum0.03
Maximum79.35
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:33.684840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile1.5855
Q18.8375
median24.87
Q340.7525
95-th percentile58.6945
Maximum79.35
Range79.32
Interquartile range (IQR)31.915

Descriptive statistics

Standard deviation18.87835463
Coefficient of variation (CV)0.7193298869
Kurtosis-0.8350676892
Mean26.24436295
Median Absolute Deviation (MAD)15.97
Skewness0.363581754
Sum38106.815
Variance356.3922736
MonotocityNot monotonic
2021-02-12T10:55:33.806954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.994
 
0.1%
2.214
 
0.1%
1.64
 
0.1%
9.234
 
0.1%
1.593
 
0.1%
1.563
 
0.1%
1.773
 
0.1%
1.793
 
0.1%
12.723
 
0.1%
1.033
 
0.1%
Other values (1265)1418
31.1%
(Missing)3110
68.2%
ValueCountFrequency (%)
0.031
< 0.1%
0.051
< 0.1%
0.071
< 0.1%
0.081
< 0.1%
0.11
< 0.1%
ValueCountFrequency (%)
79.351
< 0.1%
78.411
< 0.1%
77.271
< 0.1%
76.891
< 0.1%
75.811
< 0.1%

igm_interpretation
Categorical

MISSING

Distinct4
Distinct (%)0.3%
Missing3110
Missing (%)68.2%
Memory size35.8 KiB
Positive
1022 
Negative
346 
Equivocal
 
81
Equivocal,Positive
 
3

Length

Max length18
Median length8
Mean length8.076446281
Min length8

Characters and Unicode

Total characters11727
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive
ValueCountFrequency (%)
Positive1022
 
22.4%
Negative346
 
7.6%
Equivocal81
 
1.8%
Equivocal,Positive3
 
0.1%
(Missing)3110
68.2%
2021-02-12T10:55:34.017911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:55:34.082412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
positive1022
70.4%
negative346
 
23.8%
equivocal81
 
5.6%
equivocal,positive3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i2480
21.1%
e1717
14.6%
v1455
12.4%
t1371
11.7%
o1109
9.5%
P1025
8.7%
s1025
8.7%
a430
 
3.7%
N346
 
3.0%
g346
 
3.0%
Other values (6)423
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10269
87.6%
Uppercase Letter1455
 
12.4%
Other Punctuation3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
i2480
24.2%
e1717
16.7%
v1455
14.2%
t1371
13.4%
o1109
10.8%
s1025
10.0%
a430
 
4.2%
g346
 
3.4%
q84
 
0.8%
u84
 
0.8%
Other values (2)168
 
1.6%
ValueCountFrequency (%)
P1025
70.4%
N346
 
23.8%
E84
 
5.8%
ValueCountFrequency (%)
,3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11724
> 99.9%
Common3
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
i2480
21.2%
e1717
14.6%
v1455
12.4%
t1371
11.7%
o1109
9.5%
P1025
8.7%
s1025
8.7%
a430
 
3.7%
N346
 
3.0%
g346
 
3.0%
Other values (5)420
 
3.6%
ValueCountFrequency (%)
,3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11727
100.0%

Most frequent character per block

ValueCountFrequency (%)
i2480
21.1%
e1717
14.6%
v1455
12.4%
t1371
11.7%
o1109
9.5%
P1025
8.7%
s1025
8.7%
a430
 
3.7%
N346
 
3.0%
g346
 
3.0%
Other values (6)423
 
3.6%

jaundice
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing88
Missing (%)1.9%
Memory size35.8 KiB
False
4320 
True
 
154
(Missing)
 
88
ValueCountFrequency (%)
False4320
94.7%
True154
 
3.4%
(Missing)88
 
1.9%
2021-02-12T10:55:34.127594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

joint_pain
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3878
Missing (%)85.0%
Memory size35.8 KiB
False
527 
True
 
157
(Missing)
3878 
ValueCountFrequency (%)
False527
 
11.6%
True157
 
3.4%
(Missing)3878
85.0%
2021-02-12T10:55:34.161758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

liver_palpation_size
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)1.0%
Missing3859
Missing (%)84.6%
Infinite0
Infinite (%)0.0%
Mean0.4829302987
Minimum0
Maximum5
Zeros487
Zeros (%)10.7%
Memory size35.8 KiB
2021-02-12T10:55:34.217597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8352927081
Coefficient of variation (CV)1.729634091
Kurtosis3.044201769
Mean0.4829302987
Median Absolute Deviation (MAD)0
Skewness1.809830304
Sum339.5
Variance0.6977139082
MonotocityNot monotonic
2021-02-12T10:55:34.297099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0487
 
10.7%
1119
 
2.6%
271
 
1.6%
319
 
0.4%
1.53
 
0.1%
43
 
0.1%
51
 
< 0.1%
(Missing)3859
84.6%
ValueCountFrequency (%)
0487
10.7%
1119
 
2.6%
1.53
 
0.1%
271
 
1.6%
319
 
0.4%
ValueCountFrequency (%)
51
 
< 0.1%
43
 
0.1%
319
 
0.4%
271
1.6%
1.53
 
0.1%

lymphocytes_percent
Real number (ℝ≥0)

MISSING

Distinct627
Distinct (%)16.3%
Missing724
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean38.88203491
Minimum3.200000048
Maximum87
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:34.393802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.200000048
5-th percentile13.80000019
Q128
median38.79999924
Q350
95-th percentile63
Maximum87
Range83.79999995
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.95920645
Coefficient of variation (CV)0.3847331159
Kurtosis-0.5475444748
Mean38.88203491
Median Absolute Deviation (MAD)10.95000076
Skewness0.05246361036
Sum149229.25
Variance223.7778575
MonotocityNot monotonic
2021-02-12T10:55:34.510310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6050
 
1.1%
5050
 
1.1%
5544
 
1.0%
5943
 
0.9%
5742
 
0.9%
5442
 
0.9%
5640
 
0.9%
5840
 
0.9%
4540
 
0.9%
4935
 
0.8%
Other values (617)3412
74.8%
(Missing)724
 
15.9%
ValueCountFrequency (%)
3.2000000481
< 0.1%
3.2999999521
< 0.1%
41
< 0.1%
4.280000211
< 0.1%
4.4000000951
< 0.1%
ValueCountFrequency (%)
871
< 0.1%
85.800003051
< 0.1%
84.699996951
< 0.1%
83.300003051
< 0.1%
81.400001531
< 0.1%

melaena
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3852
Missing (%)84.4%
Memory size35.8 KiB
False
626 
True
 
84
(Missing)
3852 
ValueCountFrequency (%)
False626
 
13.7%
True84
 
1.8%
(Missing)3852
84.4%
2021-02-12T10:55:34.575477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

muscle_pain
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3878
Missing (%)85.0%
Memory size35.8 KiB
True
424 
False
 
260
(Missing)
3878 
ValueCountFrequency (%)
True424
 
9.3%
False260
 
5.7%
(Missing)3878
85.0%
2021-02-12T10:55:34.616192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

neutrophils_percent
Real number (ℝ≥0)

MISSING

Distinct740
Distinct (%)19.3%
Missing724
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean43.42040646
Minimum5
Maximum91
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:34.691164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile19
Q130.39999962
median41.40000153
Q355
95-th percentile73.11499825
Maximum91
Range86
Interquartile range (IQR)24.60000038

Descriptive statistics

Standard deviation16.58467935
Coefficient of variation (CV)0.3819558752
Kurtosis-0.4956411866
Mean43.42040646
Median Absolute Deviation (MAD)11.59999847
Skewness0.3638800648
Sum166647.52
Variance275.0515891
MonotocityNot monotonic
2021-02-12T10:55:34.799891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30103
 
2.3%
3584
 
1.8%
4068
 
1.5%
2554
 
1.2%
4549
 
1.1%
3248
 
1.1%
2043
 
0.9%
3637
 
0.8%
3836
 
0.8%
2835
 
0.8%
Other values (730)3281
71.9%
(Missing)724
 
15.9%
ValueCountFrequency (%)
51
 
< 0.1%
61
 
< 0.1%
6.51
 
< 0.1%
83
0.1%
8.1000003811
 
< 0.1%
ValueCountFrequency (%)
911
< 0.1%
89.91
< 0.1%
89.800003051
< 0.1%
89.51
< 0.1%
88.900001531
< 0.1%

oedema
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3852
Missing (%)84.4%
Memory size35.8 KiB
False
694 
True
 
16
(Missing)
3852 
ValueCountFrequency (%)
False694
 
15.2%
True16
 
0.4%
(Missing)3852
84.4%
2021-02-12T10:55:34.868925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

parental_fluid
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing101
Missing (%)2.2%
Memory size35.8 KiB
True
2852 
False
1609 
(Missing)
 
101
ValueCountFrequency (%)
True2852
62.5%
False1609
35.3%
(Missing)101
 
2.2%
2021-02-12T10:55:34.904561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

parental_fluid_period
Real number (ℝ≥0)

MISSING

Distinct43
Distinct (%)42.2%
Missing4460
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean40.51960784
Minimum12
Maximum96
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:34.982346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median38.5
Q347.5
95-th percentile71.7
Maximum96
Range84
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation15.9390736
Coefficient of variation (CV)0.3933669265
Kurtosis2.54933476
Mean40.51960784
Median Absolute Deviation (MAD)8.5
Skewness1.334049694
Sum4133
Variance254.0540672
MonotocityNot monotonic
2021-02-12T10:55:35.090564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
488
 
0.2%
447
 
0.2%
386
 
0.1%
326
 
0.1%
245
 
0.1%
365
 
0.1%
405
 
0.1%
394
 
0.1%
344
 
0.1%
223
 
0.1%
Other values (33)49
 
1.1%
(Missing)4460
97.8%
ValueCountFrequency (%)
121
 
< 0.1%
191
 
< 0.1%
203
0.1%
213
0.1%
223
0.1%
ValueCountFrequency (%)
961
< 0.1%
941
< 0.1%
881
< 0.1%
871
< 0.1%
771
< 0.1%

parental_fluid_volume
Real number (ℝ≥0)

MISSING

Distinct44
Distinct (%)43.1%
Missing4460
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean4402.745098
Minimum1500
Maximum12225
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:35.195548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile2758.5
Q13500
median4150
Q35000
95-th percentile7380
Maximum12225
Range10725
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation1654.183958
Coefficient of variation (CV)0.3757164954
Kurtosis5.810179299
Mean4402.745098
Median Absolute Deviation (MAD)740
Skewness1.955584861
Sum449080
Variance2736324.568
MonotocityNot monotonic
2021-02-12T10:55:35.313577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
350013
 
0.3%
450011
 
0.2%
30008
 
0.2%
37505
 
0.1%
50005
 
0.1%
40004
 
0.1%
42504
 
0.1%
38003
 
0.1%
32503
 
0.1%
55003
 
0.1%
Other values (34)43
 
0.9%
(Missing)4460
97.8%
ValueCountFrequency (%)
15001
< 0.1%
17001
< 0.1%
20001
< 0.1%
25002
< 0.1%
27501
< 0.1%
ValueCountFrequency (%)
122251
< 0.1%
100501
< 0.1%
90001
< 0.1%
89001
< 0.1%
83501
< 0.1%

pcr_dengue_load
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct206
Distinct (%)99.5%
Missing4355
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean303679058.4
Minimum355
Maximum1.64574 × 1010
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:35.421723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum355
5-th percentile4710.25
Q1110528.8
median2048750
Q333838020
95-th percentile1080747200
Maximum1.64574 × 1010
Range1.645739964 × 1010
Interquartile range (IQR)33727491.2

Descriptive statistics

Standard deviation1430339850
Coefficient of variation (CV)4.710037817
Kurtosis84.91422685
Mean303679058.4
Median Absolute Deviation (MAD)2044052.5
Skewness8.44514837
Sum6.28615651 × 1010
Variance2.045872085 × 1018
MonotocityNot monotonic
2021-02-12T10:55:35.538354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8625640002
 
< 0.1%
85662501
 
< 0.1%
1049438001
 
< 0.1%
2020001
 
< 0.1%
1973996001
 
< 0.1%
442253.61
 
< 0.1%
29279900001
 
< 0.1%
3976536001
 
< 0.1%
8282220001
 
< 0.1%
48792.41
 
< 0.1%
Other values (196)196
 
4.3%
(Missing)4355
95.5%
ValueCountFrequency (%)
3551
< 0.1%
3891
< 0.1%
7561
< 0.1%
1199.741
< 0.1%
15701
< 0.1%
ValueCountFrequency (%)
1.64574 × 10101
< 0.1%
83937200001
< 0.1%
52538800001
< 0.1%
50933200001
< 0.1%
29279900001
< 0.1%

pcr_dengue_reaction
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct204
Distinct (%)98.6%
Missing4355
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean679717.4301
Minimum0.7961
Maximum36900000
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:35.654697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.7961
5-th percentile5.417
Q1156.45
median4361
Q370350
95-th percentile2427000
Maximum36900000
Range36899999.2
Interquartile range (IQR)70193.55

Descriptive statistics

Standard deviation3206791.587
Coefficient of variation (CV)4.717830447
Kurtosis84.93235827
Mean679717.4301
Median Absolute Deviation (MAD)4351.699
Skewness8.445427154
Sum140701508
Variance1.028351228 × 1013
MonotocityNot monotonic
2021-02-12T10:55:35.774872image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19300002
 
< 0.1%
109.42
 
< 0.1%
268002
 
< 0.1%
50351
 
< 0.1%
25200001
 
< 0.1%
20.111
 
< 0.1%
3030001
 
< 0.1%
79261
 
< 0.1%
19701
 
< 0.1%
13.21
 
< 0.1%
Other values (194)194
 
4.3%
(Missing)4355
95.5%
ValueCountFrequency (%)
0.79611
< 0.1%
0.87211
< 0.1%
1.6961
< 0.1%
2.691
< 0.1%
3.0611
< 0.1%
ValueCountFrequency (%)
369000001
< 0.1%
188000001
< 0.1%
118000001
< 0.1%
114000001
< 0.1%
65700001
< 0.1%

petechiae
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing88
Missing (%)1.9%
Memory size35.8 KiB
True
3358 
False
1116 
(Missing)
 
88
ValueCountFrequency (%)
True3358
73.6%
False1116
 
24.5%
(Missing)88
 
1.9%
2021-02-12T10:55:35.845780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

pleural_effusion
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing88
Missing (%)1.9%
Memory size35.8 KiB
False
3919 
True
555 
(Missing)
 
88
ValueCountFrequency (%)
False3919
85.9%
True555
 
12.2%
(Missing)88
 
1.9%
2021-02-12T10:55:35.887421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

plt
Real number (ℝ≥0)

MISSING

Distinct492
Distinct (%)12.3%
Missing556
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean106.5821518
Minimum1
Maximum1170
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:35.963684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q138
median76
Q3141
95-th percentile307.75
Maximum1170
Range1169
Interquartile range (IQR)103

Descriptive statistics

Standard deviation98.18061754
Coefficient of variation (CV)0.9211731599
Kurtosis7.45535114
Mean106.5821518
Median Absolute Deviation (MAD)46
Skewness2.083230776
Sum426968.1
Variance9639.433661
MonotocityNot monotonic
2021-02-12T10:55:36.083296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3049
 
1.1%
1846
 
1.0%
1943
 
0.9%
2743
 
0.9%
1739
 
0.9%
2439
 
0.9%
4539
 
0.9%
5737
 
0.8%
3137
 
0.8%
3236
 
0.8%
Other values (482)3598
78.9%
(Missing)556
 
12.2%
ValueCountFrequency (%)
11
 
< 0.1%
22
 
< 0.1%
34
0.1%
46
0.1%
54
0.1%
ValueCountFrequency (%)
11701
< 0.1%
7441
< 0.1%
7221
< 0.1%
6771
< 0.1%
6721
< 0.1%

pulse
Real number (ℝ≥0)

MISSING

Distinct34
Distinct (%)5.0%
Missing3884
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean90.5619469
Minimum60
Maximum150
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:36.192210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile80
Q180
median90
Q3100
95-th percentile115
Maximum150
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.32073214
Coefficient of variation (CV)0.1360475626
Kurtosis2.252381022
Mean90.5619469
Median Absolute Deviation (MAD)10
Skewness0.9860570607
Sum61401
Variance151.8004405
MonotocityNot monotonic
2021-02-12T10:55:36.291390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
80172
 
3.8%
90138
 
3.0%
100113
 
2.5%
8575
 
1.6%
11025
 
0.5%
9524
 
0.5%
12021
 
0.5%
8814
 
0.3%
9611
 
0.2%
7011
 
0.2%
Other values (24)74
 
1.6%
(Missing)3884
85.1%
ValueCountFrequency (%)
609
0.2%
651
 
< 0.1%
681
 
< 0.1%
7011
0.2%
721
 
< 0.1%
ValueCountFrequency (%)
1501
 
< 0.1%
1404
 
0.1%
1303
 
0.1%
1251
 
< 0.1%
12021
0.5%

restlessness
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3868
Missing (%)84.8%
Memory size35.8 KiB
True
639 
False
 
55
(Missing)
3868 
ValueCountFrequency (%)
True639
 
14.0%
False55
 
1.2%
(Missing)3868
84.8%
2021-02-12T10:55:36.357282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

runny_nose
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3870
Missing (%)84.8%
Memory size35.8 KiB
False
622 
True
 
70
(Missing)
3870 
ValueCountFrequency (%)
False622
 
13.6%
True70
 
1.5%
(Missing)3870
84.8%
2021-02-12T10:55:36.393996image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

serotype
Categorical

MISSING

Distinct5
Distinct (%)0.8%
Missing3909
Missing (%)85.7%
Memory size35.8 KiB
<LOD
446 
DENV-1
108 
DENV-2
65 
DENV-3
 
30
DENV-4
 
4

Length

Max length6
Median length4
Mean length4.633996937
Min length4

Characters and Unicode

Total characters3026
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDENV-1
2nd rowDENV-1
3rd row<LOD
4th row<LOD
5th row<LOD
ValueCountFrequency (%)
<LOD446
 
9.8%
DENV-1108
 
2.4%
DENV-265
 
1.4%
DENV-330
 
0.7%
DENV-44
 
0.1%
(Missing)3909
85.7%
2021-02-12T10:55:36.558511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T10:55:36.617507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lod446
68.3%
denv-1108
 
16.5%
denv-265
 
10.0%
denv-330
 
4.6%
denv-44
 
0.6%

Most occurring characters

ValueCountFrequency (%)
D653
21.6%
<446
14.7%
L446
14.7%
O446
14.7%
E207
 
6.8%
N207
 
6.8%
V207
 
6.8%
-207
 
6.8%
1108
 
3.6%
265
 
2.1%
Other values (2)34
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2166
71.6%
Math Symbol446
 
14.7%
Dash Punctuation207
 
6.8%
Decimal Number207
 
6.8%

Most frequent character per category

ValueCountFrequency (%)
D653
30.1%
L446
20.6%
O446
20.6%
E207
 
9.6%
N207
 
9.6%
V207
 
9.6%
ValueCountFrequency (%)
1108
52.2%
265
31.4%
330
 
14.5%
44
 
1.9%
ValueCountFrequency (%)
-207
100.0%
ValueCountFrequency (%)
<446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2166
71.6%
Common860
 
28.4%

Most frequent character per script

ValueCountFrequency (%)
D653
30.1%
L446
20.6%
O446
20.6%
E207
 
9.6%
N207
 
9.6%
V207
 
9.6%
ValueCountFrequency (%)
<446
51.9%
-207
24.1%
1108
 
12.6%
265
 
7.6%
330
 
3.5%
44
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3026
100.0%

Most frequent character per block

ValueCountFrequency (%)
D653
21.6%
<446
14.7%
L446
14.7%
O446
14.7%
E207
 
6.8%
N207
 
6.8%
V207
 
6.8%
-207
 
6.8%
1108
 
3.6%
265
 
2.1%
Other values (2)34
 
1.1%

shock
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
False
3826 
True
736 
ValueCountFrequency (%)
False3826
83.9%
True736
 
16.1%
2021-02-12T10:55:36.664901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
False
4334 
True
 
228
ValueCountFrequency (%)
False4334
95.0%
True228
 
5.0%
2021-02-12T10:55:36.704783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

skin_rash
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing102
Missing (%)2.2%
Memory size35.8 KiB
False
3900 
True
560 
(Missing)
 
102
ValueCountFrequency (%)
False3900
85.5%
True560
 
12.3%
(Missing)102
 
2.2%
2021-02-12T10:55:36.737201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

sore_throat
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing3873
Missing (%)84.9%
Memory size35.8 KiB
False
552 
True
 
137
(Missing)
3873 
ValueCountFrequency (%)
False552
 
12.1%
True137
 
3.0%
(Missing)3873
84.9%
2021-02-12T10:55:36.774881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

vomiting
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing102
Missing (%)2.2%
Memory size35.8 KiB
False
3150 
True
1310 
(Missing)
 
102
ValueCountFrequency (%)
False3150
69.0%
True1310
28.7%
(Missing)102
 
2.2%
2021-02-12T10:55:36.808295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

wbc
Real number (ℝ≥0)

MISSING

Distinct421
Distinct (%)10.9%
Missing711
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean5.244811737
Minimum0.6999999881
Maximum49.20000076
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:36.881781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.6999999881
5-th percentile1.700000048
Q13.200000048
median4.699999809
Q36.450000048
95-th percentile10.5
Maximum49.20000076
Range48.50000077
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation3.230666296
Coefficient of variation (CV)0.615973739
Kurtosis28.29962628
Mean5.244811737
Median Absolute Deviation (MAD)1.600000381
Skewness3.453931436
Sum20197.77
Variance10.43720472
MonotocityNot monotonic
2021-02-12T10:55:37.002333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
492
 
2.0%
4.30000019177
 
1.7%
4.09999990573
 
1.6%
571
 
1.6%
4.19999980970
 
1.5%
4.69999980969
 
1.5%
2.59999990568
 
1.5%
2.79999995266
 
1.4%
4.90000009565
 
1.4%
2.562
 
1.4%
Other values (411)3138
68.8%
(Missing)711
 
15.6%
ValueCountFrequency (%)
0.69999998811
 
< 0.1%
0.89999997624
 
0.1%
111
0.2%
1.1000000248
0.2%
1.20000004816
0.4%
ValueCountFrequency (%)
49.200000761
< 0.1%
47.400001531
< 0.1%
40.51
< 0.1%
37.200000761
< 0.1%
321
< 0.1%

weight
Real number (ℝ≥0)

MISSING

Distinct59
Distinct (%)1.3%
Missing182
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean51.03995434
Minimum30
Maximum104
Zeros0
Zeros (%)0.0%
Memory size35.8 KiB
2021-02-12T10:55:37.115448image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile39
Q145
median50
Q355
95-th percentile70
Maximum104
Range74
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.995694529
Coefficient of variation (CV)0.1958405852
Kurtosis2.536921937
Mean51.03995434
Median Absolute Deviation (MAD)5
Skewness1.27942282
Sum223555
Variance99.91390911
MonotocityNot monotonic
2021-02-12T10:55:37.231024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45394
 
8.6%
50393
 
8.6%
52271
 
5.9%
40230
 
5.0%
43199
 
4.4%
55190
 
4.2%
46184
 
4.0%
42183
 
4.0%
48180
 
3.9%
47157
 
3.4%
Other values (49)1999
43.8%
(Missing)182
 
4.0%
ValueCountFrequency (%)
307
 
0.2%
325
 
0.1%
338
 
0.2%
33.57
 
0.2%
3527
0.6%
ValueCountFrequency (%)
1047
 
0.2%
985
 
0.1%
866
 
0.1%
8523
0.5%
838
 
0.2%

day_from_enrolment
Real number (ℝ)

ZEROS

Distinct89
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.581543183
Minimum-361
Maximum734
Zeros740
Zeros (%)16.2%
Memory size35.8 KiB
2021-02-12T10:55:37.350705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-361
5-th percentile-1
Q10
median2
Q34
95-th percentile18
Maximum734
Range1095
Interquartile range (IQR)4

Descriptive statistics

Standard deviation25.39602645
Coefficient of variation (CV)5.543116247
Kurtosis303.3916522
Mean4.581543183
Median Absolute Deviation (MAD)2
Skewness14.21108611
Sum20901
Variance644.9581596
MonotocityNot monotonic
2021-02-12T10:55:37.465734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0740
16.2%
1698
15.3%
2684
15.0%
3594
13.0%
4443
9.7%
-1403
8.8%
5241
 
5.3%
6136
 
3.0%
-289
 
2.0%
762
 
1.4%
Other values (79)472
10.3%
ValueCountFrequency (%)
-3611
< 0.1%
-2141
< 0.1%
-121
< 0.1%
-101
< 0.1%
-91
< 0.1%
ValueCountFrequency (%)
7341
< 0.1%
5351
< 0.1%
4411
< 0.1%
3721
< 0.1%
3711
< 0.1%

day_from_admission
Real number (ℝ)

ZEROS

Distinct95
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.931389741
Minimum-367
Maximum734
Zeros740
Zeros (%)16.2%
Memory size35.8 KiB
2021-02-12T10:55:37.578347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-367
5-th percentile0
Q11
median3
Q34
95-th percentile18
Maximum734
Range1101
Interquartile range (IQR)3

Descriptive statistics

Standard deviation28.0486529
Coefficient of variation (CV)5.687778571
Kurtosis227.7672153
Mean4.931389741
Median Absolute Deviation (MAD)2
Skewness6.838666571
Sum22497
Variance786.7269294
MonotocityNot monotonic
2021-02-12T10:55:37.699196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0740
16.2%
1698
15.3%
2688
15.1%
3652
14.3%
4561
12.3%
5358
7.8%
6207
 
4.5%
7108
 
2.4%
858
 
1.3%
1846
 
1.0%
Other values (85)446
9.8%
ValueCountFrequency (%)
-3671
< 0.1%
-3651
< 0.1%
-3641
< 0.1%
-3631
< 0.1%
-3621
< 0.1%
ValueCountFrequency (%)
7341
< 0.1%
5361
< 0.1%
4411
< 0.1%
3751
< 0.1%
3711
< 0.1%

Interactions

2021-02-12T10:54:16.915614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.006824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.112041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.203778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.292251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.371956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.456303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.554256image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.659391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.755533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.861196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:17.948739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.043097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.130537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.216539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.302446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.381979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.448315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.520246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.594479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.671850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.758126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.843780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:18.931749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.019126image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.103535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.192858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.271317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.351098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.432263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.523160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.610702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.692827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.777714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.886852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:19.979955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.072434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.159460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.236039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.320406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.398960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.471488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.548290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.613354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.685930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.785049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.868776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:20.946098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.032267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.111318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.194042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.267874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.348287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.443784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.522731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.606842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.701624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.779153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.866684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:21.959199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.047674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.137084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.265627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.387743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.499312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.595362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.682268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.776195image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.854939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.930340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:22.993597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.064270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.141829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.223253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.311833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.406539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.496575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.586072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.671655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.754823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.838572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.921910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:23.998273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.074508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.149646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.232687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.326516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.400998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.490546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.569916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.658741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.752530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.830329image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.912147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:24.990432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:25.056177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:25.123128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:25.196668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:25.273698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:25.363397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:25.467430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-12T10:54:25.554042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-02-12T10:55:23.760282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-02-12T10:55:37.833271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T10:55:38.068087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T10:55:38.291547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T10:55:38.579042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-12T10:55:39.244960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-12T10:55:24.137172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T10:55:25.864536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-12T10:55:27.007162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-12T10:55:28.652007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

study_nodateabdominal_distensionabdominal_painagealbuminaltascitesastbilirubin_directbilirubin_totalbleeding_gibleeding_gumbleeding_mucosalbleeding_nosebleeding_skinbleeding_vaginalbody_temperaturebruisingcoughcreatininediarrhoeaecchymosiseffusiongenderhaematocrit_percenthaemoglobinheadacheheart_soundheighthematemesishematomahematuriaiggigg_interpretationigmigm_interpretationjaundicejoint_painliver_palpation_sizelymphocytes_percentmelaenamuscle_painneutrophils_percentoedemaparental_fluidparental_fluid_periodparental_fluid_volumepcr_dengue_loadpcr_dengue_reactionpetechiaepleural_effusionpltpulserestlessnessrunny_noseserotypeshockshock_multipleskin_rashsore_throatvomitingwbcweightday_from_enrolmentday_from_admission
012006-09-12V_0True17.023.069.0True90.0NaNNaNTrueFalseTrueTrueTrueNaN37.0TrueFalse99.0TrueFalseTrueMale58.50000015.500000TrueTrueNaNFalseFalseFalse43.35Positive22.11PositiveFalseFalse3.015.0FalseTrue67.000000FalseTrue50.04500.014512.5011.610TrueTrue3.0100.0TrueFalseDENV-1TrueTrueFalseFalseFalse2.0050.000
112006-09-13NaNTrue17.0NaNNaNTrueNaNNaNNaNTrueFalseTrueTrueTrueNaNNaNTrueNaNNaNTrueNaNNaNMale47.00000016.299999NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaN12.0NaNNaN38.000000NaNTrueNaNNaNNaNNaNTrueTrue11.0NaNNaNNaNNaNTrueTrueFalseNaNFalse5.7050.011
212006-09-14NaNTrue17.0NaN180.0True244.0NaNNaNTrueFalseTrueTrueTrueNaNNaNTrueNaNNaNTrueNaNNaNMale40.00000014.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaN23.0NaNNaN40.000000NaNTrueNaNNaNNaNNaNTrueTrue17.0NaNNaNNaNNaNTrueTrueFalseNaNFalse4.8050.022
312006-09-15NaNTrue17.0NaNNaNTrueNaNNaNNaNTrueFalseTrueTrueTrueNaNNaNTrueNaNNaNTrueNaNNaNMale38.00000013.100000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaN25.0NaNNaN50.000000NaNTrueNaNNaNNaNNaNTrueTrue41.0NaNNaNNaNNaNTrueTrueFalseNaNFalse4.2050.033
412006-09-16NaNTrue17.0NaNNaNTrueNaNNaNNaNTrueFalseTrueTrueTrueNaNNaNTrueNaNNaNTrueNaNNaNMale44.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNTrueTrue146.0NaNNaNNaNNaNTrueTrueFalseNaNFalseNaN50.044
512006-09-18NaNTrue17.0NaNNaNTrueNaNNaNNaNTrueFalseTrueTrueTrueNaNNaNTrueNaNNaNTrueNaNNaNMale44.50000014.700000NaNNaNNaNNaNNaNNaN53.45Positive27.30PositiveFalseNaNNaN23.5NaNNaN55.000000NaNTrueNaNNaNNaNNaNTrueTrue243.0NaNNaNNaNNaNTrueTrueFalseNaNFalse6.2650.066
612006-09-29NaNTrue17.0NaN46.0True44.0NaNNaNTrueFalseTrueTrueTrueNaNNaNTrueNaNNaNTrueNaNNaNMale43.59999814.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaN11.3NaNNaN82.800003NaNTrueNaNNaNNaNNaNTrueTrue324.0NaNNaNNaNNaNTrueTrueFalseNaNFalse6.6050.01717
722006-09-15NaNTrue24.0NaNNaNTrueNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMale65.000000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNTrueTrue78.0NaNNaNNaNNaNTrueTrueFalseNaNFalseNaN57.0-10
822006-09-16V_1True24.0NaN1815.0True605.0NaNNaNTrueFalseTrueFalseTrueNaN37.0TrueFalse101.0FalseFalseTrueMale56.50000016.600000TrueFalseNaNTrueFalseFalse49.16Positive33.77PositiveFalseFalse0.09.6FalseTrue85.599998FalseTrue38.05750.06998.755.599TrueTrue131.0120.0TrueFalseDENV-1TrueTrueFalseFalseFalse12.5057.001
922006-09-17NaNTrue24.0NaNNaNTrueNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMale36.50000012.400000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaN9.0NaNNaN77.300003NaNTrueNaNNaNNaNNaNTrueTrue185.0NaNNaNNaNNaNTrueTrueFalseNaNFalse27.4057.012

Last rows

study_nodateabdominal_distensionabdominal_painagealbuminaltascitesastbilirubin_directbilirubin_totalbleeding_gibleeding_gumbleeding_mucosalbleeding_nosebleeding_skinbleeding_vaginalbody_temperaturebruisingcoughcreatininediarrhoeaecchymosiseffusiongenderhaematocrit_percenthaemoglobinheadacheheart_soundheighthematemesishematomahematuriaiggigg_interpretationigmigm_interpretationjaundicejoint_painliver_palpation_sizelymphocytes_percentmelaenamuscle_painneutrophils_percentoedemaparental_fluidparental_fluid_periodparental_fluid_volumepcr_dengue_loadpcr_dengue_reactionpetechiaepleural_effusionpltpulserestlessnessrunny_noseserotypeshockshock_multipleskin_rashsore_throatvomitingwbcweightday_from_enrolmentday_from_admission
45527382008-10-27NaNFalse28.0NaNNaNFalseNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMale49.94999918.600000NaNNaN172.0NaNNaNNaNNaNNaNNaNNaNFalseNaNNaN20.900000NaNNaN62.700001NaNTrueNaNNaNNaNNaNTrueFalse1.0NaNNaNNaNNaNFalseFalseFalseNaNFalse3.685.0-13
45537382008-10-28V_0False28.034.700001NaNFalseNaNNaNNaNTrueFalseTrueFalseTrueNaN37.5TrueFalse70.0FalseFalseFalseMale47.09999816.799999TrueTrue172.0FalseTrueTrue37.42Positive51.90PositiveFalseFalse0.049.000000FalseTrue40.000000FalseTrueNaNNaNNaNNaNTrueFalse5.090.0TrueFalse<LODFalseFalseFalseFalseFalse5.685.004
45547382008-10-29NaNFalse28.0NaNNaNFalseNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMale37.70000113.900000NaNNaN172.0NaNNaNNaNNaNNaNNaNNaNFalseNaNNaN9.100000NaNNaN64.000000NaNTrueNaNNaNNaNNaNTrueFalse11.0NaNNaNNaNNaNFalseFalseFalseNaNFalse16.585.015
45557382008-10-30NaNFalse28.0NaNNaNFalseNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMale35.70000112.900000NaNNaN172.0NaNNaNNaNNaNNaNNaNNaNFalseNaNNaN9.500000NaNNaN70.300003NaNTrueNaNNaNNaNNaNTrueFalse41.0NaNNaNNaNNaNFalseFalseFalseNaNFalse15.585.026
45567382008-10-31NaNFalse28.0NaNNaNFalseNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMale36.79999913.000000NaNNaN172.0NaNNaNNaN48.91Positive62.11PositiveFalseNaNNaN13.200000NaNNaN63.099998NaNTrueNaNNaNNaNNaNTrueFalse121.0NaNNaNNaNNaNFalseFalseFalseNaNFalse10.985.037
45577382008-11-01NaNFalse28.0NaNNaNFalseNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMale47.299999NaNNaNNaN172.0NaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNTrueFalse182.0NaNNaNNaNNaNFalseFalseFalseNaNFalseNaN85.048
45587382008-11-02NaNFalse28.0NaNNaNFalseNaNNaNNaNTrueFalseTrueFalseTrueNaNNaNTrueNaNNaNFalseNaNNaNMaleNaNNaNNaNNaN172.0NaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNTrueFalseNaNNaNNaNNaNNaNFalseFalseFalseNaNFalseNaN85.059
45597392008-10-28NaNFalse25.0NaNNaNFalseNaNNaNNaNNaNFalseTrueFalseTrueNaNNaNFalseNaNNaNFalseNaNNaNMaleNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNTrueNaNNaNNaNNaNTrueFalseNaNNaNNaNNaNNaNFalseFalseFalseNaNFalseNaN52.0-10
45607392008-10-29V_0False25.043.90000211.0False16.0NaNNaNNaNFalseTrueFalseTrueNaN39.0FalseTrue60.0FalseFalseFalseMale30.400000NaNTrueFalseNaNFalseFalseFalse1.92Negative1.60NegativeFalseFalse0.04.700000FalseFalse91.000000FalseTrueNaNNaNNaNNaNTrueFalse2.0110.0TrueFalse<LODFalseFalseFalseFalseFalse15.252.001
45617402008-11-12V_0True15.0NaN24.0False58.0NaNNaNTrueFalseTrueFalseTrueNaNNaNTrueFalse64.0FalseFalseFalseMale26.0000008.100000TrueTrue153.0TrueFalseFalse3.90Negative39.69PositiveFalseFalse0.018.799999TrueTrue77.900002FalseTrueNaNNaN8260000.018500.0TrueFalse56.0104.0TrueFalseDENV-1FalseFalseFalseFalseTrue3.138.000